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Quick Overview
Reflex is an open-source framework for building web applications using Python. It allows developers to create full-stack, reactive web apps without needing to write JavaScript or HTML, leveraging the power and simplicity of Python for both frontend and backend development.
Pros
- Enables full-stack web development using only Python
- Reactive programming model for building dynamic user interfaces
- Seamless integration of frontend and backend code
- Rapid development and prototyping capabilities
Cons
- Learning curve for developers new to reactive programming concepts
- Limited ecosystem compared to more established web frameworks
- Potential performance overhead due to Python-based frontend
- May not be suitable for highly complex or large-scale applications
Code Examples
- Creating a simple counter app:
import reflex as rx
class State(rx.State):
count: int = 0
def increment(self):
self.count += 1
def index():
return rx.vstack(
rx.heading(State.count),
rx.button("Increment", on_click=State.increment),
)
app = rx.App()
app.add_page(index)
- Fetching and displaying data from an API:
import reflex as rx
import httpx
class State(rx.State):
data: list = []
async def fetch_data(self):
async with httpx.AsyncClient() as client:
response = await client.get("https://api.example.com/data")
self.data = response.json()
def index():
return rx.vstack(
rx.button("Fetch Data", on_click=State.fetch_data),
rx.foreach(State.data, lambda item: rx.text(item)),
)
app = rx.App()
app.add_page(index)
- Creating a simple form:
import reflex as rx
class State(rx.State):
name: str = ""
email: str = ""
def submit(self):
print(f"Submitted: {self.name}, {self.email}")
def index():
return rx.vstack(
rx.input(placeholder="Name", on_change=State.set_name),
rx.input(placeholder="Email", on_change=State.set_email),
rx.button("Submit", on_click=State.submit),
)
app = rx.App()
app.add_page(index)
Getting Started
To get started with Reflex, follow these steps:
-
Install Reflex:
pip install reflex
-
Create a new Reflex project:
reflex init my_app cd my_app
-
Run the development server:
reflex run
Your app will be available at http://localhost:3000
. Edit the my_app/my_app.py
file to start building your application.
Competitor Comparisons
Streamlit — A faster way to build and share data apps.
Pros of Streamlit
- Larger community and ecosystem with more resources and third-party components
- Simpler learning curve for beginners, especially those familiar with Python
- Better documentation and more comprehensive examples
Cons of Streamlit
- Less flexibility in UI customization and layout control
- Performance can be slower for complex applications due to its architecture
- Limited support for advanced frontend features and interactivity
Code Comparison
Streamlit:
import streamlit as st
st.title("Hello World")
name = st.text_input("Enter your name")
st.write(f"Hello, {name}!")
Reflex:
import reflex as rx
def index():
return rx.vstack(
rx.heading("Hello World"),
rx.input(placeholder="Enter your name"),
rx.text("Hello, {state.name}!")
)
app = rx.App()
app.add_page(index)
Both frameworks aim to simplify web app development for Python developers, but Reflex offers more control over the frontend at the cost of a steeper learning curve. Streamlit is ideal for quick prototypes and data-focused apps, while Reflex is better suited for building more complex, interactive web applications with a native feel.
Data Apps & Dashboards for Python. No JavaScript Required.
Pros of Dash
- Mature and well-established framework with extensive documentation
- Large community and ecosystem of extensions
- Seamless integration with Plotly for interactive data visualization
Cons of Dash
- Steeper learning curve, especially for those new to web development
- More verbose code compared to newer frameworks
- Limited built-in styling options, often requiring custom CSS
Code Comparison
Dash:
import dash
import dash_core_components as dcc
import dash_html_components as html
app = dash.Dash(__name__)
app.layout = html.Div([
html.H1('Hello Dash'),
dcc.Graph(id='example-graph')
])
Reflex:
import reflex as rx
def index():
return rx.Box(
rx.Heading("Hello Reflex"),
rx.Plot(...)
)
app = rx.App()
app.add_page(index)
Reflex offers a more concise and Pythonic approach to building web apps, with a focus on simplicity and ease of use. It provides a unified API for both frontend and backend development, reducing the need for separate HTML and JavaScript knowledge. However, Dash benefits from its maturity, extensive documentation, and seamless integration with Plotly for data visualization tasks.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Pros of Gradio
- Simpler setup and faster prototyping for ML models
- Wider range of pre-built components for various input/output types
- More extensive documentation and community support
Cons of Gradio
- Less flexibility for complex UI layouts and custom designs
- Limited to Python backend, whereas Reflex supports full-stack development
- Steeper learning curve for advanced customization
Code Comparison
Gradio example:
import gradio as gr
def greet(name):
return f"Hello, {name}!"
demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()
Reflex example:
import reflex as rx
class State(rx.State):
name: str = "World"
def index():
return rx.vstack(
rx.input(value=State.name, on_change=State.set_name),
rx.text(f"Hello, {State.name}!")
)
app = rx.App()
app.add_page(index)
Both frameworks aim to simplify the creation of web interfaces for ML models and applications. Gradio excels in rapid prototyping and out-of-the-box components, while Reflex offers more flexibility for full-stack development and complex UI designs. The choice between them depends on the specific project requirements and developer preferences.
Panel: The powerful data exploration & web app framework for Python
Pros of Panel
- Mature and well-established project with a large community and extensive documentation
- Supports a wide range of data visualization libraries and frameworks
- Offers both server-side and client-side rendering options
Cons of Panel
- Steeper learning curve, especially for developers new to Python data visualization
- Less focus on modern web development practices and reactive programming paradigms
- May require more boilerplate code for complex applications
Code Comparison
Panel:
import panel as pn
import numpy as np
def plot(n):
return pn.pane.Matplotlib(plt.plot(np.random.randn(n)))
pn.interact(plot, n=pn.widgets.IntSlider(start=1, end=100))
Reflex:
import reflex as rx
import numpy as np
import matplotlib.pyplot as plt
def index():
plot = rx.plot(plt.plot(np.random.randn(50)))
return rx.vstack(plot, rx.slider(1, 100, on_change=lambda v: plot.update(plt.plot(np.random.randn(v)))))
app = rx.App()
app.add_page(index)
Both examples create an interactive plot with a slider, but Reflex's approach is more declarative and reactive.
🕸️ Web apps in pure Python 🐍
Pros of Reflex
- More active development with frequent updates and improvements
- Larger community and better documentation
- Supports a wider range of features and use cases
Cons of Reflex
- Potentially more complex setup and learning curve
- May have more dependencies and a larger footprint
- Could be overkill for simpler projects
Code Comparison
Reflex:
import reflex as rx
def index():
return rx.text("Hello, Reflex!")
app = rx.App()
app.add_page(index)
Reflex>:
from reflex import Reflex
def hello():
return "Hello, Reflex>!"
app = Reflex(__name__)
app.route("/")(hello)
Both repositories aim to provide a framework for building web applications in Python, but Reflex appears to be the more mature and feature-rich option. Reflex> seems to have a simpler syntax and may be easier to get started with for beginners. However, Reflex offers more advanced features and better long-term support due to its larger community and active development. The choice between the two would depend on the specific requirements of your project and your familiarity with web development concepts.
Flet enables developers to easily build realtime web, mobile and desktop apps in Python. No frontend experience required.
Pros of Flet
- Supports multiple programming languages (Python, Go, C#)
- Offers both desktop and web deployment options
- Provides a simpler, more straightforward API for UI development
Cons of Flet
- Less focus on reactive programming paradigms
- More limited customization options for advanced UI designs
- Smaller community and ecosystem compared to Reflex
Code Comparison
Flet example:
import flet as ft
def main(page: ft.Page):
page.add(ft.Text("Hello, World!"))
ft.app(target=main)
Reflex example:
import reflex as rx
def index():
return rx.text("Hello, World!")
app = rx.App()
app.add_page(index)
Both Flet and Reflex aim to simplify UI development for Python developers, but they take different approaches. Flet focuses on a more traditional, imperative style of programming, while Reflex emphasizes reactive programming concepts. Flet's multi-language support and desktop deployment options make it versatile, but Reflex's reactive approach may be more suitable for complex, data-driven applications. The choice between the two depends on the specific project requirements and the developer's preferred programming style.
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+ Searching for Pynecone? You are in the right repo. Pynecone has been renamed to Reflex. +
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Reflex
Reflex is a library to build full-stack web apps in pure Python.
Key features:
- Pure Python - Write your app's frontend and backend all in Python, no need to learn Javascript.
- Full Flexibility - Reflex is easy to get started with, but can also scale to complex apps.
- Deploy Instantly - After building, deploy your app with a single command or host it on your own server.
See our architecture page to learn how Reflex works under the hood.
âï¸ Installation
Open a terminal and run (Requires Python 3.9+):
pip install reflex
𥳠Create your first app
Installing reflex
also installs the reflex
command line tool.
Test that the install was successful by creating a new project. (Replace my_app_name
with your project name):
mkdir my_app_name
cd my_app_name
reflex init
This command initializes a template app in your new directory.
You can run this app in development mode:
reflex run
You should see your app running at http://localhost:3000.
Now you can modify the source code in my_app_name/my_app_name.py
. Reflex has fast refreshes so you can see your changes instantly when you save your code.
𫧠Example App
Let's go over an example: creating an image generation UI around DALL·E. For simplicity, we just call the OpenAI API, but you could replace this with an ML model run locally.

Here is the complete code to create this. This is all done in one Python file!
import reflex as rx
import openai
openai_client = openai.OpenAI()
class State(rx.State):
"""The app state."""
prompt = ""
image_url = ""
processing = False
complete = False
def get_image(self):
"""Get the image from the prompt."""
if self.prompt == "":
return rx.window_alert("Prompt Empty")
self.processing, self.complete = True, False
yield
response = openai_client.images.generate(
prompt=self.prompt, n=1, size="1024x1024"
)
self.image_url = response.data[0].url
self.processing, self.complete = False, True
def index():
return rx.center(
rx.vstack(
rx.heading("DALL-E", font_size="1.5em"),
rx.input(
placeholder="Enter a prompt..",
on_blur=State.set_prompt,
width="25em",
),
rx.button(
"Generate Image",
on_click=State.get_image,
width="25em",
loading=State.processing
),
rx.cond(
State.complete,
rx.image(src=State.image_url, width="20em"),
),
align="center",
),
width="100%",
height="100vh",
)
# Add state and page to the app.
app = rx.App()
app.add_page(index, title="Reflex:DALL-E")
Let's break this down.

Reflex UI
Let's start with the UI.
def index():
return rx.center(
...
)
This index
function defines the frontend of the app.
We use different components such as center
, vstack
, input
, and button
to build the frontend. Components can be nested within each other
to create complex layouts. And you can use keyword args to style them with the full power of CSS.
Reflex comes with 60+ built-in components to help you get started. We are actively adding more components, and it's easy to create your own components.
State
Reflex represents your UI as a function of your state.
class State(rx.State):
"""The app state."""
prompt = ""
image_url = ""
processing = False
complete = False
The state defines all the variables (called vars) in an app that can change and the functions that change them.
Here the state is comprised of a prompt
and image_url
. There are also the booleans processing
and complete
to indicate when to disable the button (during image generation) and when to show the resulting image.
Event Handlers
def get_image(self):
"""Get the image from the prompt."""
if self.prompt == "":
return rx.window_alert("Prompt Empty")
self.processing, self.complete = True, False
yield
response = openai_client.images.generate(
prompt=self.prompt, n=1, size="1024x1024"
)
self.image_url = response.data[0].url
self.processing, self.complete = False, True
Within the state, we define functions called event handlers that change the state vars. Event handlers are the way that we can modify the state in Reflex. They can be called in response to user actions, such as clicking a button or typing in a text box. These actions are called events.
Our DALL·E. app has an event handler, get_image
to which get this image from the OpenAI API. Using yield
in the middle of an event handler will cause the UI to update. Otherwise the UI will update at the end of the event handler.
Routing
Finally, we define our app.
app = rx.App()
We add a page from the root of the app to the index component. We also add a title that will show up in the page preview/browser tab.
app.add_page(index, title="DALL-E")
You can create a multi-page app by adding more pages.
ð Resources
ð Docs | ðï¸ Blog | ð± Component Library | ð¼ï¸ Templates | ð¸ Deployment
â Status
Reflex launched in December 2022 with the name Pynecone.
As of February 2024, our hosting service is in alpha! During this time anyone can deploy their apps for free. See our roadmap to see what's planned.
Reflex has new releases and features coming every week! Make sure to :star: star and :eyes: watch this repository to stay up to date.
Contributing
We welcome contributions of any size! Below are some good ways to get started in the Reflex community.
- Join Our Discord: Our Discord is the best place to get help on your Reflex project and to discuss how you can contribute.
- GitHub Discussions: A great way to talk about features you want added or things that are confusing/need clarification.
- GitHub Issues: Issues are an excellent way to report bugs. Additionally, you can try and solve an existing issue and submit a PR.
We are actively looking for contributors, no matter your skill level or experience. To contribute check out CONTIBUTING.md
All Thanks To Our Contributors:
License
Reflex is open-source and licensed under the Apache License 2.0.
Top Related Projects
Streamlit — A faster way to build and share data apps.
Data Apps & Dashboards for Python. No JavaScript Required.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Panel: The powerful data exploration & web app framework for Python
🕸️ Web apps in pure Python 🐍
Flet enables developers to easily build realtime web, mobile and desktop apps in Python. No frontend experience required.
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designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
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